Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations4177
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory465.1 KiB
Average record size in memory114.0 B

Variable types

Categorical1
Numeric8

Alerts

Diameter is highly overall correlated with Height and 6 other fieldsHigh correlation
Height is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Length is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Rings is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shell_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shucked_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Viscera_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Whole_weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation

Reproduction

Analysis started2024-12-14 05:49:43.986226
Analysis finished2024-12-14 05:49:52.750224
Duration8.76 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Sex
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
M
1528 
I
1342 
F
1307 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4177
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowM
5th rowI

Common Values

ValueCountFrequency (%)
M 1528
36.6%
I 1342
32.1%
F 1307
31.3%

Length

2024-12-14T11:19:52.825110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-14T11:19:52.944447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
m 1528
36.6%
i 1342
32.1%
f 1307
31.3%

Most occurring characters

ValueCountFrequency (%)
M 1528
36.6%
I 1342
32.1%
F 1307
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1528
36.6%
I 1342
32.1%
F 1307
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1528
36.6%
I 1342
32.1%
F 1307
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1528
36.6%
I 1342
32.1%
F 1307
31.3%

Length
Real number (ℝ)

High correlation 

Distinct134
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5239921
Minimum0.075
Maximum0.815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:53.092151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.295
Q10.45
median0.545
Q30.615
95-th percentile0.69
Maximum0.815
Range0.74
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.12009291
Coefficient of variation (CV)0.2291884
Kurtosis0.064620974
Mean0.5239921
Median Absolute Deviation (MAD)0.08
Skewness-0.63987327
Sum2188.715
Variance0.014422308
MonotonicityNot monotonic
2024-12-14T11:19:53.242022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.625 94
 
2.3%
0.55 94
 
2.3%
0.575 93
 
2.2%
0.58 92
 
2.2%
0.6 87
 
2.1%
0.62 87
 
2.1%
0.5 81
 
1.9%
0.57 79
 
1.9%
0.63 78
 
1.9%
0.61 75
 
1.8%
Other values (124) 3317
79.4%
ValueCountFrequency (%)
0.075 1
 
< 0.1%
0.11 1
 
< 0.1%
0.13 2
 
< 0.1%
0.135 1
 
< 0.1%
0.14 2
 
< 0.1%
0.15 1
 
< 0.1%
0.155 3
0.1%
0.16 4
0.1%
0.165 5
0.1%
0.17 3
0.1%
ValueCountFrequency (%)
0.815 1
 
< 0.1%
0.8 1
 
< 0.1%
0.78 2
 
< 0.1%
0.775 2
 
< 0.1%
0.77 3
 
0.1%
0.765 2
 
< 0.1%
0.76 2
 
< 0.1%
0.755 3
 
0.1%
0.75 8
0.2%
0.745 5
0.1%

Diameter
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40788125
Minimum0.055
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:53.380307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.22
Q10.35
median0.425
Q30.48
95-th percentile0.545
Maximum0.65
Range0.595
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.099239866
Coefficient of variation (CV)0.24330578
Kurtosis-0.045475581
Mean0.40788125
Median Absolute Deviation (MAD)0.065
Skewness-0.60919814
Sum1703.72
Variance0.009848551
MonotonicityNot monotonic
2024-12-14T11:19:53.510628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45 139
 
3.3%
0.475 120
 
2.9%
0.4 111
 
2.7%
0.5 110
 
2.6%
0.47 100
 
2.4%
0.48 91
 
2.2%
0.455 90
 
2.2%
0.46 89
 
2.1%
0.44 87
 
2.1%
0.485 83
 
2.0%
Other values (101) 3157
75.6%
ValueCountFrequency (%)
0.055 1
 
< 0.1%
0.09 1
 
< 0.1%
0.095 1
 
< 0.1%
0.1 2
 
< 0.1%
0.105 4
0.1%
0.11 4
0.1%
0.115 2
 
< 0.1%
0.12 5
0.1%
0.125 7
0.2%
0.13 8
0.2%
ValueCountFrequency (%)
0.65 1
 
< 0.1%
0.63 3
 
0.1%
0.625 1
 
< 0.1%
0.62 1
 
< 0.1%
0.615 1
 
< 0.1%
0.61 1
 
< 0.1%
0.605 3
 
0.1%
0.6 8
0.2%
0.595 4
0.1%
0.59 6
0.1%

Height
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395164
Minimum0
Maximum1.13
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:53.657130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.115
median0.14
Q30.165
95-th percentile0.2
Maximum1.13
Range1.13
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.041827057
Coefficient of variation (CV)0.29980029
Kurtosis76.025509
Mean0.1395164
Median Absolute Deviation (MAD)0.025
Skewness3.1288174
Sum582.76
Variance0.0017495027
MonotonicityNot monotonic
2024-12-14T11:19:53.802472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 267
 
6.4%
0.14 220
 
5.3%
0.155 217
 
5.2%
0.175 211
 
5.1%
0.16 205
 
4.9%
0.125 202
 
4.8%
0.165 193
 
4.6%
0.135 189
 
4.5%
0.145 182
 
4.4%
0.12 169
 
4.0%
Other values (41) 2122
50.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 1
 
< 0.1%
0.015 2
 
< 0.1%
0.02 2
 
< 0.1%
0.025 5
 
0.1%
0.03 6
 
0.1%
0.035 6
 
0.1%
0.04 13
0.3%
0.045 11
0.3%
0.05 18
0.4%
ValueCountFrequency (%)
1.13 1
 
< 0.1%
0.515 1
 
< 0.1%
0.25 3
 
0.1%
0.24 4
 
0.1%
0.235 6
 
0.1%
0.23 10
 
0.2%
0.225 13
0.3%
0.22 17
0.4%
0.215 31
0.7%
0.21 23
0.6%

Whole_weight
Real number (ℝ)

High correlation 

Distinct2429
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82874216
Minimum0.002
Maximum2.8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:53.948663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.1259
Q10.4415
median0.7995
Q31.153
95-th percentile1.6949
Maximum2.8255
Range2.8235
Interquartile range (IQR)0.7115

Descriptive statistics

Standard deviation0.49038902
Coefficient of variation (CV)0.59172689
Kurtosis-0.023643504
Mean0.82874216
Median Absolute Deviation (MAD)0.3565
Skewness0.53095856
Sum3461.656
Variance0.24048139
MonotonicityNot monotonic
2024-12-14T11:19:54.092009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2225 8
 
0.2%
0.4775 7
 
0.2%
0.196 7
 
0.2%
0.97 7
 
0.2%
1.1345 7
 
0.2%
0.18 6
 
0.1%
0.6765 6
 
0.1%
0.5805 6
 
0.1%
0.494 6
 
0.1%
0.3245 6
 
0.1%
Other values (2419) 4111
98.4%
ValueCountFrequency (%)
0.002 1
< 0.1%
0.008 1
< 0.1%
0.0105 1
< 0.1%
0.013 1
< 0.1%
0.014 1
< 0.1%
0.0145 2
< 0.1%
0.015 1
< 0.1%
0.0155 1
< 0.1%
0.0175 1
< 0.1%
0.018 2
< 0.1%
ValueCountFrequency (%)
2.8255 1
< 0.1%
2.7795 1
< 0.1%
2.657 1
< 0.1%
2.555 1
< 0.1%
2.55 1
< 0.1%
2.548 1
< 0.1%
2.526 1
< 0.1%
2.5155 1
< 0.1%
2.5085 1
< 0.1%
2.505 1
< 0.1%

Shucked_weight
Real number (ℝ)

High correlation 

Distinct1515
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35936749
Minimum0.001
Maximum1.488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:54.237866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0524
Q10.186
median0.336
Q30.502
95-th percentile0.7402
Maximum1.488
Range1.487
Interquartile range (IQR)0.316

Descriptive statistics

Standard deviation0.22196295
Coefficient of variation (CV)0.61764894
Kurtosis0.59512368
Mean0.35936749
Median Absolute Deviation (MAD)0.1585
Skewness0.71909792
Sum1501.078
Variance0.049267551
MonotonicityNot monotonic
2024-12-14T11:19:54.468998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.175 11
 
0.3%
0.2505 10
 
0.2%
0.302 9
 
0.2%
0.2945 9
 
0.2%
0.096 9
 
0.2%
0.21 9
 
0.2%
0.165 9
 
0.2%
0.419 9
 
0.2%
0.2 9
 
0.2%
0.2025 9
 
0.2%
Other values (1505) 4084
97.8%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.0025 1
 
< 0.1%
0.0045 2
< 0.1%
0.005 3
0.1%
0.0055 2
< 0.1%
0.0065 3
0.1%
0.007 1
 
< 0.1%
0.0075 4
0.1%
0.008 1
 
< 0.1%
0.0085 1
 
< 0.1%
ValueCountFrequency (%)
1.488 1
< 0.1%
1.351 1
< 0.1%
1.3485 1
< 0.1%
1.253 1
< 0.1%
1.2455 1
< 0.1%
1.2395 2
< 0.1%
1.232 1
< 0.1%
1.1965 1
< 0.1%
1.1945 1
< 0.1%
1.1705 1
< 0.1%

Viscera_weight
Real number (ℝ)

High correlation 

Distinct880
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18059361
Minimum0.0005
Maximum0.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:54.602539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.027
Q10.0935
median0.171
Q30.253
95-th percentile0.3796
Maximum0.76
Range0.7595
Interquartile range (IQR)0.1595

Descriptive statistics

Standard deviation0.10961425
Coefficient of variation (CV)0.60696639
Kurtosis0.084011749
Mean0.18059361
Median Absolute Deviation (MAD)0.0795
Skewness0.59185215
Sum754.3395
Variance0.012015284
MonotonicityNot monotonic
2024-12-14T11:19:54.740697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1715 15
 
0.4%
0.196 14
 
0.3%
0.0575 13
 
0.3%
0.037 13
 
0.3%
0.061 13
 
0.3%
0.2195 13
 
0.3%
0.099 12
 
0.3%
0.096 12
 
0.3%
0.15 12
 
0.3%
0.0265 12
 
0.3%
Other values (870) 4048
96.9%
ValueCountFrequency (%)
0.0005 2
 
< 0.1%
0.002 1
 
< 0.1%
0.0025 2
 
< 0.1%
0.003 3
0.1%
0.0035 3
0.1%
0.004 1
 
< 0.1%
0.0045 4
0.1%
0.005 7
0.2%
0.0055 6
0.1%
0.006 2
 
< 0.1%
ValueCountFrequency (%)
0.76 1
< 0.1%
0.6415 1
< 0.1%
0.59 1
< 0.1%
0.575 1
< 0.1%
0.5745 1
< 0.1%
0.564 1
< 0.1%
0.55 1
< 0.1%
0.541 2
< 0.1%
0.5265 1
< 0.1%
0.526 1
< 0.1%

Shell_weight
Real number (ℝ)

High correlation 

Distinct926
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23883086
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:54.885041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0384
Q10.13
median0.234
Q30.329
95-th percentile0.48
Maximum1.005
Range1.0035
Interquartile range (IQR)0.199

Descriptive statistics

Standard deviation0.13920267
Coefficient of variation (CV)0.58285043
Kurtosis0.53192613
Mean0.23883086
Median Absolute Deviation (MAD)0.0995
Skewness0.62092683
Sum997.5965
Variance0.019377383
MonotonicityNot monotonic
2024-12-14T11:19:55.030226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.275 43
 
1.0%
0.25 42
 
1.0%
0.315 40
 
1.0%
0.185 40
 
1.0%
0.265 40
 
1.0%
0.17 37
 
0.9%
0.285 37
 
0.9%
0.175 36
 
0.9%
0.22 36
 
0.9%
0.3 36
 
0.9%
Other values (916) 3790
90.7%
ValueCountFrequency (%)
0.0015 1
 
< 0.1%
0.003 1
 
< 0.1%
0.0035 1
 
< 0.1%
0.004 2
 
< 0.1%
0.005 12
0.3%
0.006 1
 
< 0.1%
0.0065 1
 
< 0.1%
0.007 1
 
< 0.1%
0.0075 1
 
< 0.1%
0.008 4
 
0.1%
ValueCountFrequency (%)
1.005 1
 
< 0.1%
0.897 1
 
< 0.1%
0.885 2
< 0.1%
0.85 1
 
< 0.1%
0.815 1
 
< 0.1%
0.7975 1
 
< 0.1%
0.78 1
 
< 0.1%
0.76 1
 
< 0.1%
0.726 1
 
< 0.1%
0.725 3
0.1%

Rings
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9336845
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.8 KiB
2024-12-14T11:19:55.148095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median9
Q311
95-th percentile16
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.224169
Coefficient of variation (CV)0.3245693
Kurtosis2.3306874
Mean9.9336845
Median Absolute Deviation (MAD)2
Skewness1.1141019
Sum41493
Variance10.395266
MonotonicityNot monotonic
2024-12-14T11:19:55.270530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9 689
16.5%
10 634
15.2%
8 568
13.6%
11 487
11.7%
7 391
9.4%
12 267
 
6.4%
6 259
 
6.2%
13 203
 
4.9%
14 126
 
3.0%
5 115
 
2.8%
Other values (18) 438
10.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 15
 
0.4%
4 57
 
1.4%
5 115
 
2.8%
6 259
 
6.2%
7 391
9.4%
8 568
13.6%
9 689
16.5%
10 634
15.2%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 2
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 9
 
0.2%
22 6
 
0.1%
21 14
0.3%
20 26
0.6%
19 32
0.8%

Interactions

2024-12-14T11:19:51.497231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.217747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.176304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.130396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:47.231587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:48.581818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.679609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.604838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.618331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.342459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.293798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.282829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:47.406820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:48.725579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.808327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.712233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.719952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.470100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.406099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.427727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:47.565514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:48.879731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.930571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.813034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.820702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.577822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.521158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.563942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:47.717751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.014837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.038240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.914014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.919322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.696629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.629250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.690183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:47.989455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.164298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.153909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.031298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:52.120068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.815542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.742059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.827790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:48.158233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.300678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.277331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.150599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:52.224062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:44.938304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.868649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.968414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:48.310341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.433908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.401068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.273654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:52.336262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:45.055534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:46.006956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:47.096975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:48.443927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:49.544142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:50.503539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-14T11:19:51.379527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-14T11:19:55.381214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
DiameterHeightLengthRingsSexShell_weightShucked_weightViscera_weightWhole_weight
Diameter1.0000.8960.9830.6230.4020.9540.9500.9480.971
Height0.8961.0000.8880.6580.3600.9210.8740.9010.916
Length0.9830.8881.0000.6040.3940.9480.9570.9530.973
Rings0.6230.6580.6041.0000.3560.6920.5390.6140.631
Sex0.4020.3600.3940.3561.0000.4130.3930.4240.424
Shell_weight0.9540.9210.9480.6920.4131.0000.9180.9380.969
Shucked_weight0.9500.8740.9570.5390.3930.9181.0000.9480.977
Viscera_weight0.9480.9010.9530.6140.4240.9380.9481.0000.975
Whole_weight0.9710.9160.9730.6310.4240.9690.9770.9751.000

Missing values

2024-12-14T11:19:52.490485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-14T11:19:52.661947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SexLengthDiameterHeightWhole_weightShucked_weightViscera_weightShell_weightRings
0M0.4550.3650.0950.51400.22450.10100.15015
1M0.3500.2650.0900.22550.09950.04850.0707
2F0.5300.4200.1350.67700.25650.14150.2109
3M0.4400.3650.1250.51600.21550.11400.15510
4I0.3300.2550.0800.20500.08950.03950.0557
5I0.4250.3000.0950.35150.14100.07750.1208
6F0.5300.4150.1500.77750.23700.14150.33020
7F0.5450.4250.1250.76800.29400.14950.26016
8M0.4750.3700.1250.50950.21650.11250.1659
9F0.5500.4400.1500.89450.31450.15100.32019
SexLengthDiameterHeightWhole_weightShucked_weightViscera_weightShell_weightRings
4167M0.5000.3800.1250.57700.26900.12650.15359
4168F0.5150.4000.1250.61500.28650.12300.17658
4169M0.5200.3850.1650.79100.37500.18000.181510
4170M0.5500.4300.1300.83950.31550.19550.240510
4171M0.5600.4300.1550.86750.40000.17200.22908
4172F0.5650.4500.1650.88700.37000.23900.249011
4173M0.5900.4400.1350.96600.43900.21450.260510
4174M0.6000.4750.2051.17600.52550.28750.30809
4175F0.6250.4850.1501.09450.53100.26100.296010
4176M0.7100.5550.1951.94850.94550.37650.495012